With applications increasingly moving to the cloud, it is becoming common for an application to be separated by the network from the I/O devices with which the user is interacting. Currently this requires modifying the application to receive user input from the network rather than the device. We present a new I/O architecture in which the device driver is split into two parts, with the network between them. This architecture makes the network invisible to both device and application, allowing both of them to work unmodified. Our architecture also supports transformation modules, each of which comes in a pair that operates on each side of the network. Via these module pairs, the resulting system is capable of supporting the modification of the I/O stream in a variety of ways to compensate for the network, while remaining transparent to the application.
{"title":"A Remote I/O Solution for the Cloud","authors":"C. Taylor, J. Pasquale","doi":"10.1109/CLOUD.2012.116","DOIUrl":"https://doi.org/10.1109/CLOUD.2012.116","url":null,"abstract":"With applications increasingly moving to the cloud, it is becoming common for an application to be separated by the network from the I/O devices with which the user is interacting. Currently this requires modifying the application to receive user input from the network rather than the device. We present a new I/O architecture in which the device driver is split into two parts, with the network between them. This architecture makes the network invisible to both device and application, allowing both of them to work unmodified. Our architecture also supports transformation modules, each of which comes in a pair that operates on each side of the network. Via these module pairs, the resulting system is capable of supporting the modification of the I/O stream in a variety of ways to compensate for the network, while remaining transparent to the application.","PeriodicalId":214084,"journal":{"name":"2012 IEEE Fifth International Conference on Cloud Computing","volume":"286 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132584611","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The volume of worldwide digital content has increased nine-fold within the last five years, and this immense growth is predicted to continue in foreseeable future reaching 8ZB already by 2015. Traditionally, in order to cope with the growing demand for storage capacity, organizations proactively built and managed their private storage facilities. Recently, with the proliferation of public cloud infrastructure offerings, many organizations, instead, welcomed the alternative of outsourcing their storage needs to the providers of public cloud storage services. The comparative cost-efficiency of these two alternatives depends on a number of factors, among which are e.g. the prices of the public and private storage, the charging and the storage acquisition intervals, and the predictability of the demand for storage. In this paper, we study how the cost-efficiency of the private vs. public storage depends on the acquisition interval at which the organization re-assesses its storage needs and acquires additional private storage. The analysis in the paper suggests that the shorter the acquisition interval, the more likely it is that the private storage solution is less expensive as compared with the public cloud infrastructure. This phenomenon is also illustrated in the paper numerically using the storage needs encountered by a university back-up and archiving service as an example. Since the acquisition interval is determined by the organization's ability to foresee the growth of storage demand, by the provisioning schedules of storage equipment providers, and by internal practices of the organization, among other factors, the organization owning a private storage solution may want to control some of these factors in order to attain a shorter acquisition interval and thus make the private storage (more) cost-efficient.
{"title":"Impact of Storage Acquisition Intervals on the Cost-Efficiency of the Private vs. Public Storage","authors":"O. Mazhelis, Gabriella Fazekas, P. Tyrväinen","doi":"10.1109/CLOUD.2012.101","DOIUrl":"https://doi.org/10.1109/CLOUD.2012.101","url":null,"abstract":"The volume of worldwide digital content has increased nine-fold within the last five years, and this immense growth is predicted to continue in foreseeable future reaching 8ZB already by 2015. Traditionally, in order to cope with the growing demand for storage capacity, organizations proactively built and managed their private storage facilities. Recently, with the proliferation of public cloud infrastructure offerings, many organizations, instead, welcomed the alternative of outsourcing their storage needs to the providers of public cloud storage services. The comparative cost-efficiency of these two alternatives depends on a number of factors, among which are e.g. the prices of the public and private storage, the charging and the storage acquisition intervals, and the predictability of the demand for storage. In this paper, we study how the cost-efficiency of the private vs. public storage depends on the acquisition interval at which the organization re-assesses its storage needs and acquires additional private storage. The analysis in the paper suggests that the shorter the acquisition interval, the more likely it is that the private storage solution is less expensive as compared with the public cloud infrastructure. This phenomenon is also illustrated in the paper numerically using the storage needs encountered by a university back-up and archiving service as an example. Since the acquisition interval is determined by the organization's ability to foresee the growth of storage demand, by the provisioning schedules of storage equipment providers, and by internal practices of the organization, among other factors, the organization owning a private storage solution may want to control some of these factors in order to attain a shorter acquisition interval and thus make the private storage (more) cost-efficient.","PeriodicalId":214084,"journal":{"name":"2012 IEEE Fifth International Conference on Cloud Computing","volume":"117 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132907415","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Tobias Binz, Christoph Fehling, F. Leymann, Alexander Nowak, D. Schumm
Enterprises often have no integrated and comprehensive view of their enterprise topology describing their entire IT infrastructure, software, on-premise and off-premise services, processes, and their interrelations. Especially due to acquisitions, mergers, reorganizations, and outsourcing there is no clear 'big picture' of the enterprise topology. Through this lack, management of applications becomes harder and duplication of components and information systems increases. Furthermore, the lack of insight makes changes in the enterprise topology like consolidation, migration, or outsourcing more complex and error prone which leads to high operational cost. In this paper we propose Enterprise Topology Graphs (ETG) as formal model to describe an enterprise topology. Based on established graph theory ETG bring formalization and provability to the cloud. They enable the application of proven graph algorithms to solve enterprise topology research problems in general and cloud research problems in particular. For example, we present a search algorithm which locates segments in large and possibly distributed enterprise topologies using structural queries. To illustrate the power of the ETG approach we show how it can be applied for IT consolidation to reduce operational costs, increase flexibility by simplifying changes in the enterprise topology, and improve the environmental impact of the enterprise IT.
{"title":"Formalizing the Cloud through Enterprise Topology Graphs","authors":"Tobias Binz, Christoph Fehling, F. Leymann, Alexander Nowak, D. Schumm","doi":"10.1109/CLOUD.2012.143","DOIUrl":"https://doi.org/10.1109/CLOUD.2012.143","url":null,"abstract":"Enterprises often have no integrated and comprehensive view of their enterprise topology describing their entire IT infrastructure, software, on-premise and off-premise services, processes, and their interrelations. Especially due to acquisitions, mergers, reorganizations, and outsourcing there is no clear 'big picture' of the enterprise topology. Through this lack, management of applications becomes harder and duplication of components and information systems increases. Furthermore, the lack of insight makes changes in the enterprise topology like consolidation, migration, or outsourcing more complex and error prone which leads to high operational cost. In this paper we propose Enterprise Topology Graphs (ETG) as formal model to describe an enterprise topology. Based on established graph theory ETG bring formalization and provability to the cloud. They enable the application of proven graph algorithms to solve enterprise topology research problems in general and cloud research problems in particular. For example, we present a search algorithm which locates segments in large and possibly distributed enterprise topologies using structural queries. To illustrate the power of the ETG approach we show how it can be applied for IT consolidation to reduce operational costs, increase flexibility by simplifying changes in the enterprise topology, and improve the environmental impact of the enterprise IT.","PeriodicalId":214084,"journal":{"name":"2012 IEEE Fifth International Conference on Cloud Computing","volume":"79 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133263378","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Clouds are becoming the preferred platforms for large-scale applications. Currently, Cloud environments focus on high scalability and availability by relaxing consistency. Weak consistency's considered to be sufficient for most of the currently deployed applications in the Cloud. However, the Cloud is increasingly being promoted as environment for running a wide range of different types of applications on top of replicated data - of which not all will be satisfied with weak consistency. Strong consistency, even though demanded by applications, decreases availability and is costly to enforce from both a performance and monetary point of view. On the other hand, weak consistency may generate high costs due to the access to inconsistent data. In this paper, we present a novel approach, called cost-based concurrency control (C3), that allows to dynamically and adaptively switch at runtime between different consistency levels of transactions. C3 has been implemented in a Data-as-a-Service Cloud environment and considers all costs that incur during execution. These costs are determined by infrastructure costs for running a transaction in a certain consistency level (called consistency costs) and, optionally, by additional application-specific costs for compensating the effects of accessing inconsistent data (called inconsistency costs).C3 considers transaction mixes running different consistency levels at the same time while enforcing the inherent consistency guarantees of each of these protocols. The main contribution of this paper is threefold. First, it thoroughly analyzes the consistency costs of the most common concurrency control protocols; second, it specifies a set of rules that allow to dynamically select the most appropriate consistency level with the goal of minimizing the overall costs (consistency and inconsistency costs);third, it provides a protocol that guarantees that anomalies in the transaction mixes supported by C3 are avoided and that enforces the correct execution of all transactions in a transaction mix. We have evaluated C3 on the basis of real infrastructure costs, derived from Amazon's EC2. The results demonstrate the feasibility of the cost model and show that C3 leads to a reduction of the overall costs of transactions compared to a fixed consistency level.
{"title":"Cost-Based Data Consistency in a Data-as-a-Service Cloud Environment","authors":"Ilir Fetai, H. Schuldt","doi":"10.1109/CLOUD.2012.38","DOIUrl":"https://doi.org/10.1109/CLOUD.2012.38","url":null,"abstract":"Clouds are becoming the preferred platforms for large-scale applications. Currently, Cloud environments focus on high scalability and availability by relaxing consistency. Weak consistency's considered to be sufficient for most of the currently deployed applications in the Cloud. However, the Cloud is increasingly being promoted as environment for running a wide range of different types of applications on top of replicated data - of which not all will be satisfied with weak consistency. Strong consistency, even though demanded by applications, decreases availability and is costly to enforce from both a performance and monetary point of view. On the other hand, weak consistency may generate high costs due to the access to inconsistent data. In this paper, we present a novel approach, called cost-based concurrency control (C3), that allows to dynamically and adaptively switch at runtime between different consistency levels of transactions. C3 has been implemented in a Data-as-a-Service Cloud environment and considers all costs that incur during execution. These costs are determined by infrastructure costs for running a transaction in a certain consistency level (called consistency costs) and, optionally, by additional application-specific costs for compensating the effects of accessing inconsistent data (called inconsistency costs).C3 considers transaction mixes running different consistency levels at the same time while enforcing the inherent consistency guarantees of each of these protocols. The main contribution of this paper is threefold. First, it thoroughly analyzes the consistency costs of the most common concurrency control protocols; second, it specifies a set of rules that allow to dynamically select the most appropriate consistency level with the goal of minimizing the overall costs (consistency and inconsistency costs);third, it provides a protocol that guarantees that anomalies in the transaction mixes supported by C3 are avoided and that enforces the correct execution of all transactions in a transaction mix. We have evaluated C3 on the basis of real infrastructure costs, derived from Amazon's EC2. The results demonstrate the feasibility of the cost model and show that C3 leads to a reduction of the overall costs of transactions compared to a fixed consistency level.","PeriodicalId":214084,"journal":{"name":"2012 IEEE Fifth International Conference on Cloud Computing","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133972316","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Bikash Sharma, R. Prabhakar, Seung-Hwan Lim, M. Kandemir, C. Das
Efficient resource management in data centers and clouds running large distributed data processing frameworks like MapReduce is crucial for enhancing the performance of hosted applications and increasing resource utilization. However, existing resource scheduling schemes in Hadoop MapReduce allocate resources at the granularity of fixed-size, static portions of nodes, called slots. In this work, we show that MapReduce jobs have widely varying demands for multiple resources, making the static and fixed-size slot-level resource allocation a poor choice both from the performance and resource utilization standpoints. Furthermore, lack of coordination in the management of multiple resources across nodes prevents dynamic slot reconfiguration, and leads to resource contention. Motivated by this, we propose MROrchestrator, a MapReduce resource Orchestrator framework, which can dynamically identify resource bottlenecks, and resolve them through fine-grained, coordinated, and on-demand resource allocations. We have implemented MROrchestrator on two 24-node native and virtualized Hadoop clusters. Experimental results with a suite of representative MapReduce benchmarks demonstrate up to 38% reduction in job completion times, and up to 25% increase in resource utilization. We further demonstrate the performance boost in existing resource managers like NGM and Mesos, when augmented with MROrchestrator.
{"title":"MROrchestrator: A Fine-Grained Resource Orchestration Framework for MapReduce Clusters","authors":"Bikash Sharma, R. Prabhakar, Seung-Hwan Lim, M. Kandemir, C. Das","doi":"10.1109/CLOUD.2012.37","DOIUrl":"https://doi.org/10.1109/CLOUD.2012.37","url":null,"abstract":"Efficient resource management in data centers and clouds running large distributed data processing frameworks like MapReduce is crucial for enhancing the performance of hosted applications and increasing resource utilization. However, existing resource scheduling schemes in Hadoop MapReduce allocate resources at the granularity of fixed-size, static portions of nodes, called slots. In this work, we show that MapReduce jobs have widely varying demands for multiple resources, making the static and fixed-size slot-level resource allocation a poor choice both from the performance and resource utilization standpoints. Furthermore, lack of coordination in the management of multiple resources across nodes prevents dynamic slot reconfiguration, and leads to resource contention. Motivated by this, we propose MROrchestrator, a MapReduce resource Orchestrator framework, which can dynamically identify resource bottlenecks, and resolve them through fine-grained, coordinated, and on-demand resource allocations. We have implemented MROrchestrator on two 24-node native and virtualized Hadoop clusters. Experimental results with a suite of representative MapReduce benchmarks demonstrate up to 38% reduction in job completion times, and up to 25% increase in resource utilization. We further demonstrate the performance boost in existing resource managers like NGM and Mesos, when augmented with MROrchestrator.","PeriodicalId":214084,"journal":{"name":"2012 IEEE Fifth International Conference on Cloud Computing","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115192961","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Chunqing Chen, Shixing Yan, Guopeng Zhao, Bu-Sung Lee, S. Singhal
The fast growth of cloud service offerings has attracted more enterprises to migrate their IT applications into cloud. Nonetheless, complex enterprise user requirements, especially interdependent relations across them, raise new challenges of cloud service selection. In addition, a major concern for these enterprises is ensuring compliance with their policies on the use of cloud services. In this paper, we present a systematic framework, based on formal verification and constraint solving techniques, to help enterprises tackle problems when adopting cloud computing. Our framework enables automatic detection of conflicts covering violation of enterprise policies and inconsistency of user requirements, and explanation generation which identifies problematic user requirements. The framework next select automatically cloud services which satisfy all enterprise policies and user requirements (with interdependent relations). We have prototyped and successfully applied our approach to projects which manage heterogeneous cloud infrastructure services for large enterprises.
{"title":"A Systematic Framework Enabling Automatic Conflict Detection and Explanation in Cloud Service Selection for Enterprises","authors":"Chunqing Chen, Shixing Yan, Guopeng Zhao, Bu-Sung Lee, S. Singhal","doi":"10.1109/CLOUD.2012.95","DOIUrl":"https://doi.org/10.1109/CLOUD.2012.95","url":null,"abstract":"The fast growth of cloud service offerings has attracted more enterprises to migrate their IT applications into cloud. Nonetheless, complex enterprise user requirements, especially interdependent relations across them, raise new challenges of cloud service selection. In addition, a major concern for these enterprises is ensuring compliance with their policies on the use of cloud services. In this paper, we present a systematic framework, based on formal verification and constraint solving techniques, to help enterprises tackle problems when adopting cloud computing. Our framework enables automatic detection of conflicts covering violation of enterprise policies and inconsistency of user requirements, and explanation generation which identifies problematic user requirements. The framework next select automatically cloud services which satisfy all enterprise policies and user requirements (with interdependent relations). We have prototyped and successfully applied our approach to projects which manage heterogeneous cloud infrastructure services for large enterprises.","PeriodicalId":214084,"journal":{"name":"2012 IEEE Fifth International Conference on Cloud Computing","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126679955","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
With the recent introduction of Spot Instances in the Amazon Elastic Compute Cloud (EC2), users can bid for resources and thus control the balance of reliability versus monetary costs. Mechanisms and tools that deal with the cost-reliability trade-offs under this schema are of great value for users seeking to lessen their costs while maintaining high reliability. In this paper, we propose a set of bidding strategies to minimize the cost and volatility of resource provisioning. Essentially, to derive an optimal bidding strategy, we formulate this problem as a Constrained Markov Decision Process (CMDP). Based on this model, we are able to obtain an optimal randomized bidding strategy through linear programming. Using real Instance Price traces and workload models, we compare several adaptive check-pointing schemes in terms of monetary costs and job completion time. We evaluate our model and demonstrate how users should bid optimally on Spot Instances to reach different objectives with desired levels of confidence.
{"title":"Towards Optimal Bidding Strategy for Amazon EC2 Cloud Spot Instance","authors":"Shaojie Tang, Jing Yuan, Xiangyang Li","doi":"10.1109/CLOUD.2012.134","DOIUrl":"https://doi.org/10.1109/CLOUD.2012.134","url":null,"abstract":"With the recent introduction of Spot Instances in the Amazon Elastic Compute Cloud (EC2), users can bid for resources and thus control the balance of reliability versus monetary costs. Mechanisms and tools that deal with the cost-reliability trade-offs under this schema are of great value for users seeking to lessen their costs while maintaining high reliability. In this paper, we propose a set of bidding strategies to minimize the cost and volatility of resource provisioning. Essentially, to derive an optimal bidding strategy, we formulate this problem as a Constrained Markov Decision Process (CMDP). Based on this model, we are able to obtain an optimal randomized bidding strategy through linear programming. Using real Instance Price traces and workload models, we compare several adaptive check-pointing schemes in terms of monetary costs and job completion time. We evaluate our model and demonstrate how users should bid optimally on Spot Instances to reach different objectives with desired levels of confidence.","PeriodicalId":214084,"journal":{"name":"2012 IEEE Fifth International Conference on Cloud Computing","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127139658","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
R. Neumann, Steve Taggeselle, R. Dumke, A. Schmietendorf, F. Muhß, Anja Fiegler
Non-relational Cloud Storage Services, such as Windows Azure's Table Storage, promise high scalability and nearby constant response times, even with an increasing number of concurrent transactions. Measurement data that is examined throughout this paper, however, reveals a fairly high number of query performance anomalies as well as a drop in response time with an increasing entity size. To address these issues, we propose an intermediate storage service, namely the Hybrid Cloud Storage Framework (HCSF), which combines the performance advantage of the Azure Distributed Cache with the data integrity of Table Storage. The paper concludes with a performance benchmark of the HCSF with Azure's Table Storage.
{"title":"Combining Query Performance with Data Integrity in the Cloud: A Hybrid Cloud Storage Framework to Enhance Data Access on the Windows Azure Platform","authors":"R. Neumann, Steve Taggeselle, R. Dumke, A. Schmietendorf, F. Muhß, Anja Fiegler","doi":"10.1109/CLOUD.2012.62","DOIUrl":"https://doi.org/10.1109/CLOUD.2012.62","url":null,"abstract":"Non-relational Cloud Storage Services, such as Windows Azure's Table Storage, promise high scalability and nearby constant response times, even with an increasing number of concurrent transactions. Measurement data that is examined throughout this paper, however, reveals a fairly high number of query performance anomalies as well as a drop in response time with an increasing entity size. To address these issues, we propose an intermediate storage service, namely the Hybrid Cloud Storage Framework (HCSF), which combines the performance advantage of the Azure Distributed Cache with the data integrity of Table Storage. The paper concludes with a performance benchmark of the HCSF with Azure's Table Storage.","PeriodicalId":214084,"journal":{"name":"2012 IEEE Fifth International Conference on Cloud Computing","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123815805","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The integration of cloud and grid infrastructures is still of current interest, because it provides a way for the scientific area to ensure sustainability of well engineered grid applications. The integration of well established grid infrastructures with cloud systems also fosters their complementary usage, simplified migration of applications, as well as efficient resource utilization. In this paper, we compare the layered conceptual grid model to the service model of clouds. Based on this comparison, we describe pragmatic possibilities to integrate cloud and grid systems. We analyze the connectivity options on the infrastructure level to gain access to both infrastructures using a unified client. In two case studies, we show the successful integration of the Amazon Web Services cloud with UNICORE~6 and the open source cloud Eucalyptus with Globus Toolkit~4. Based on these implementations, we discuss lessons learned.
云和网格基础设施的集成仍然是当前的热点，因为它为科学领域提供了一种方法，以确保精心设计的网格应用程序的可持续性。建立良好的网格基础设施与云系统的集成也促进了它们的互补使用，简化了应用程序的迁移，以及有效的资源利用。本文将分层概念网格模型与云的服务模型进行了比较。基于这种比较，我们描述了整合云和网格系统的实用可能性。我们分析基础设施级别的连接性选项，以便使用统一的客户端访问两个基础设施。在两个案例研究中，我们展示了Amazon Web Services云与UNICORE~6的成功集成，以及开源云Eucalyptus与Globus Toolkit~4的成功集成。基于这些实现，我们讨论了经验教训。
{"title":"Pragmatic Integration of Cloud and Grid Computing Infrastructures","authors":"T. Rings, J. Grabowski","doi":"10.1109/CLOUD.2012.77","DOIUrl":"https://doi.org/10.1109/CLOUD.2012.77","url":null,"abstract":"The integration of cloud and grid infrastructures is still of current interest, because it provides a way for the scientific area to ensure sustainability of well engineered grid applications. The integration of well established grid infrastructures with cloud systems also fosters their complementary usage, simplified migration of applications, as well as efficient resource utilization. In this paper, we compare the layered conceptual grid model to the service model of clouds. Based on this comparison, we describe pragmatic possibilities to integrate cloud and grid systems. We analyze the connectivity options on the infrastructure level to gain access to both infrastructures using a unified client. In two case studies, we show the successful integration of the Amazon Web Services cloud with UNICORE~6 and the open source cloud Eucalyptus with Globus Toolkit~4. Based on these implementations, we discuss lessons learned.","PeriodicalId":214084,"journal":{"name":"2012 IEEE Fifth International Conference on Cloud Computing","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123850922","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
CPU and device virtualization technology allows applications to be hosted on cloud platforms; some of the resulting benefits are lower cost and greater elasticity. In such cloud hosted applications, some components reside on the cloud while others, such as end users and components tied to physical devices, are located outside the cloud. Many applications, e.g., telecom services, have stringent latency requirements in terms of within how much time certain procedures must be completed. The application latency is strongly determined by the locations of all the interacting components that are both within and outside the cloud. In this paper, we study the problem of determining the optimal placement of the application components in the cloud so that the latency requirements of the application can be met. We present a precise formulation of the placement problem which includes a specification of the cloud platform, and collective latency expressions for application-level latency requirements. We show that Message Sequence Charts (MSCs), a widely-used mechanism for describing the execution of application procedures, can be naturally translated into our formalism of collective latency expressions. We present placement algorithms that exploit the Euclidean triangular inequality property of network topologies: (a) an exact algorithm for determining the most optimal placement but which has a worst-case exponential running time, and (b) an algorithm for determining a close to-optimal placement that has a fast polynomial running time. Additionally, we present an exact technique for partitioning a placement problem into smaller sub problems so that greater efficiency and accuracy can be achieved. We evaluate the performance of the algorithms on a representative telecom application --- a distributed deployment of the LTE Mobility Management Entity (MME). Our evaluation results show that our approximate algorithm can outperform a random placement by up to 49% for finding a successful placement.
{"title":"Placement in Clouds for Application-Level Latency Requirements","authors":"Fangzhe Chang, R. Viswanathan, Thomas L. Wood","doi":"10.1109/CLOUD.2012.91","DOIUrl":"https://doi.org/10.1109/CLOUD.2012.91","url":null,"abstract":"CPU and device virtualization technology allows applications to be hosted on cloud platforms; some of the resulting benefits are lower cost and greater elasticity. In such cloud hosted applications, some components reside on the cloud while others, such as end users and components tied to physical devices, are located outside the cloud. Many applications, e.g., telecom services, have stringent latency requirements in terms of within how much time certain procedures must be completed. The application latency is strongly determined by the locations of all the interacting components that are both within and outside the cloud. In this paper, we study the problem of determining the optimal placement of the application components in the cloud so that the latency requirements of the application can be met. We present a precise formulation of the placement problem which includes a specification of the cloud platform, and collective latency expressions for application-level latency requirements. We show that Message Sequence Charts (MSCs), a widely-used mechanism for describing the execution of application procedures, can be naturally translated into our formalism of collective latency expressions. We present placement algorithms that exploit the Euclidean triangular inequality property of network topologies: (a) an exact algorithm for determining the most optimal placement but which has a worst-case exponential running time, and (b) an algorithm for determining a close to-optimal placement that has a fast polynomial running time. Additionally, we present an exact technique for partitioning a placement problem into smaller sub problems so that greater efficiency and accuracy can be achieved. We evaluate the performance of the algorithms on a representative telecom application --- a distributed deployment of the LTE Mobility Management Entity (MME). Our evaluation results show that our approximate algorithm can outperform a random placement by up to 49% for finding a successful placement.","PeriodicalId":214084,"journal":{"name":"2012 IEEE Fifth International Conference on Cloud Computing","volume":"95 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115691911","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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